Methods for gas leak detection and localization in populated areas using multi-point analysis

ABSTRACT

Improved gas leak detection from moving platforms is provided. Automatic horizontal spatial scale analysis can be performed in order to distinguish a leak from background levels of the measured gas. Source identification can be provided by using isotopic ratios and/or chemical tracers to distinguish gas leaks from other sources of the measured gas. Multi-point measurements combined with spatial analysis of the multi-point measurement results can provide leak source distance estimates. These methods can be practiced individually or in any combination.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. provisional patentapplication 61/627,915, filed on Oct. 20, 2011, entitled “Methods forgas leak detection and localization in populated areas”, and herebyincorporated by reference in its entirety. This application also claimsthe benefit of U.S. provisional patent application 61/646,487, filed onMay 14, 2012, entitled “Gas detection systems and methods”, and herebyincorporated by reference in its entirety.

FIELD OF THE INVENTION

This invention relates to gas leak detection.

BACKGROUND

Gas leak detection is an important practical problem. In many cases, itis desirable to rapidly search for gas leaks over a large region. Oneapproach that has been considered for such applications is to mount agas leak detection instrument on a moving vehicle, e.g., as consideredin U.S. Pat. No. 3,107,517, U.S. Pat. No. 3,444,721, and U.S. Pat. No.4,164,138. However, conventional approaches for mobile gas leakdetection suffer from significant disadvantages. Typically, thesedisadvantages include one or more of: 1) difficulty in distinguishing aleak from background, 2) difficulty with distinguishing a leak fromother possible sources of the measured gas, and 3) lack of an estimateddistance to the leak source.

Accordingly, it would be an advance in the art to overcome thesedifficulties.

SUMMARY

The present approach alleviates these difficulties in the followingmanner. Automatic horizontal spatial scale analysis can be performed inorder to distinguish a leak from background levels of the measured gas.Source identification can be provided by using isotopic ratios and/orchemical tracers to distinguish gas leaks from other sources of themeasured gas. Multi-point measurements combined with spatial analysis ofthe multi-point measurement results can provide leak source distanceestimates. These methods can be practiced individually or in anycombination.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1a-b schematically show horizontal analysis according toembodiments of the invention.

FIG. 2 schematically shows an exemplary optical absorption instrumentsuitable for use with embodiments of the invention.

FIGS. 3a-b show isotopic analysis results from an embodiment of theinvention.

FIG. 4 shows a gas handling approach suitable for use in connection withisotope ratio measurements.

FIGS. 5a-c schematically show multi-point measurements according toembodiments of the invention.

FIGS. 6a-c schematically show some gas handling approaches suitable formulti-point measurements.

FIG. 7 shows an exemplary user interface display relating to embodimentsof the invention.

DETAILED DESCRIPTION

It is convenient to define a gas leak as being any situation where gasis present in the environment in above-background concentrations. Gasleaks as defined include, but are not limited to: leaks from gas pipesor transportation systems (e.g., natural gas leaks), leaks from gasprocessing or handling facilities, and emissions from gas sources intothe environment (e.g., pollution, gas emission from landfills, etc.).

A gas plume model is any mathematical model that relates gasconcentration to position in space.

A) Horizontal Analysis A1) Principles

FIGS. 1a-b show an example of horizontal spatial scale analysisaccording to embodiments of the invention. A moving platform 102proceeds along at least one platform track 106. Platform 102 can be anyvehicle, such as a car, truck, van, or bicycle. Platform 102 can also beany other mobile entity capable of transporting the gas measurementinstrument, such as a person, pack animal, etc. Platform track 106 isdisposed near one or more potential gas leak location (e.g., 108 a, 108b). For simplicity, the platform track is shown as a single linesegment, but in practice the platform track can be any combination ofcurves and line segments. In this example, a leak at location 108 aemits a gas plume 110 that intersects platform track 106. A gasmeasurement instrument 104 is disposed on the platform. Practice of theinvention does not depend critically on details of the gas inlet toinstrument 104. One implementation is to place this inlet at the frontof the platform as close to ground level as is practical, with one ormore discrete inlet ports (or a diffusive inlet) that span the width ofthe platform. One or more primary gas concentration measurements areperformed with instrument 104.

Typically, these primary gas concentration measurements are originallyrecorded as concentration vs. time. Platform position vs. time data(e.g., using the Global Positioning System (GPS)) is combined with theconcentration vs. time data to provide concentration vs. position data,schematically shown on FIG. 1b . Here a peak 112 and a background level114 are shown.

The availability of concentration vs. position data enables automatichorizontal spatial scale analysis, which is useful for distinguishinggas leaks from background gas levels. In general, horizontal spatialscale analysis includes any analysis approach that makes use ofconcentration vs. platform position data for gas leak detection. Adetailed example is given below. Note that simple thresholding (i.e.,reporting a leak if measured concentration is greater than X, and notreporting a leak if the measured concentration is less than X, where Xis some predetermined threshold value) is not an example of horizontalspatial scale analysis because no use is made of concentration vs.position data. Results of the automatic horizontal spatial scaleanalysis can be reported to an end user. Various approaches for thisreporting are described below. One possibility is to provide a binaryyes/no indication of whether or not a leak is present.

Horizontal Spatial Scale Analysis relies on the fact that nearby pointsources vary rapidly with changing position as the platform moves,whereas distant sources vary more slowly, due to the larger spatialextent of the emission plume. In other words, narrow spikes inconcentration just a few meters wide are generated very close to theplatform. The narrow spatial extent is used to bias nearby sources inthe leak identification process. There are several possible algorithmsfor performing horizontal spatial scale analysis, including but notlimited to:

Peak finding and width analysis—the data can be analyzed using standardpeak-location methods, and then each identified peak can be subsequentlyfit (using linear or nonlinear optimization) for center and width. Thefunctional form used for this fitting step might be a Gaussian pulse (aGaussian is the expected functional form taken by plumes propagatingthrough the atmosphere), or the convolution of a Gaussian and the systemresponse (which is typically a narrow Gaussian convolved with anexponential tail).

Spatial peak wavelet analysis—this algorithm uses a special model basisfunction (related to the discrete second derivative of the overallpoint-source system response function) that is parameterized by itswidth or spatial extent. This basis function set is convolved with themeasurement data. The output wavelet analysis gives both the horizontalposition and the effective width, which may be related via a gas plumemodel to the distance from the measurement to the emission source.

Preferably, the automatic horizontal spatial scale analysis isresponsive to gas concentration peak half-widths in a detection rangefrom about 1 m to about 50 m, and is substantially not responsive to gasconcentration peak half-widths outside of the detection range. Thisspatial selectivity helps distinguish gas leaks from variations inbackground gas concentration. For example gas background concentrationcan vary significantly (e.g., by a factor of 2 or more), but thisvariation tends to be over a significantly larger spatial length scalethan the above detection range. Note also that such large variations inbackground concentration significantly interfere with simplethresholding for finding gas leaks.

Primary gas concentration measurements are preferably performed rapidly(e.g., at a rate of 0.2 Hz or greater, more preferably 1 Hz or greater).This enables the concept of driving a vehicular platform at normalsurface street speeds (e.g., 35 miles per hour) while accumulatinguseful concentration vs. position data. If the gas concentrationmeasurements are too slow, spatial resolution of the data willundesirably be reduced. Preferably, platform position measurements areperformed at least as rapidly as the primary gas concentrationmeasurements.

Other significant attributes of the primary concentration measurementinclude:

1) The primary gas measurement analyte should be present in significantquantities for all leaks to be targeted by this method.

2) The typical background levels of this analyte in the environmentwhere these measurements are made (e.g., urban) should be sufficientlylow that the concentration change from the targeted leaks can be clearlydistinguished from the local background signals at a distance of 10-300meters.3) For natural gas, methane is the most abundant constituent, but otherhydrocarbons or other species (hydrogen sulfide or other odorants) areviable analytes for the primary concentration measurement.

The present invention does not depend critically on the gas detectiontechnology employed. Any gas detection approach capable of providingrapid trace gas concentration measurements can be employed for theprimary gas concentration measurements. One suitable gas detectionapproach is schematically shown on FIG. 2. Here the primary gasconcentration measurements are optical absorption measurements made in aresonant optical cavity disposed in an instrument in the movingplatform. More specifically, FIG. 2 shows an absorption cell 202 capableof holding a gas sample for analysis. Absorption cell 202 includes anoptical cavity defined by mirrors 204, 206, and 208. This example showsa ring cavity with a uni-directional cavity mode 208 that propagatesclockwise around the cavity. Any other resonant cavity geometry can beemployed. Cavity absorption can be measured by comparing output light212 to input light 210. Alternatively, cavity absorption can be measuredby measuring the decay rate of optical radiation emitted from the cavity(i.e., cavity ring-down spectroscopy (ORDS)).

Horizontal spatial scale analysis can be combined with the use oftracers (isotope ratio tracers and/or chemical tracers) in order toprovide leak source identification. Further details relating to tracersare provided in section B below.

Horizontal spatial scale analysis can also be combined with multi-pointmeasurements and analysis as described in section C below. The resultingautomatic transverse spatial scale analysis can provide a distanceestimate to a leak source.

Although the primary gas concentration measurements are performed whilethe platform is moving, additional concentration measurements can beperformed while the platform is stationary. Such stationary gasconcentration measurements may be useful for checking background gasconcentrations.

While real-time measurements are preferred, post analysis of moresparsely sampled data (e.g., via vacuum flask sampling and lateranalysis via gas chromatography or other methods) can also be a viablemethod for correctly identifying target emissions from other backgroundsources.

Optionally, the system can include a source of atmosphericmeteorological information, especially wind direction, but also windspeed or atmospheric stability class, either on-board the platform or ata nearby stationary location. ‘Nearby’ means close enough that theatmospheric conditions at the location of the platform arewell-correlated to the stationary measurements.

Optionally, the system can include an on-board video camera and loggingsystem that can be used to reject potential sources on the basis of thelocal imagery collected along with the concentration data. For example,a measured emissions spike could be discounted if a vehicle powered bynatural gas passed nearby during the measurements.

The platform track should be as close to potential sources as possible.With decreasing distance to the emission source: 1) The primaryconcentration signal will increase, allowing for better confidence insource identification and/or more sensitive leak detection; 2) Theeffect of wind to hide signals or shift the measured location relativeto the leak location is reduced; and 3) The spatial extent of theconcentration signal from the leak becomes narrower, making it easier todistinguish from background signals and the plumes from more distant (orextended) sources, which have much more slowly varying signals.

Optionally, repeated measurements of a single location can be made toprovide further confirmation (or rejection) of potential leaks.

Optionally, measurements can be made on different sides of the road orin different lanes to provide more precise localization of the leaksource.

Optionally, the present approach can be used in conjunction with otherconventional methods, such as visual inspection and/or measurements withhandheld meters to detect emitted constituents, to further refine theresults.

Optionally, measurements can be made at reduced speed, or parked at ornear the source, to provide additional information on location and/orsource attribution.

A2) Example

This section give a specific example of horizontal spatial scaleanalysis in connection with methane gas leak detection.

The methane concentration is measured initially as a function of time.It is combined with the output of the GPS receiver in order to obtainthe methane concentration as a function of distance from some initialpoint. Interpolation can be used to sample the data on a regularlyspaced collection of points.

The concentration of methane typically varies smoothly with position,for the most part being equal to the worldwide background level of 1.8parts per million together with enhancements from large and relativelydistant sources such as landfills and marshes. These enhancements canraise the background level by several parts per million. By contrast, atypical natural gas leak produces a plume of methane which is quitenarrow in spatial extent. Although it varies with the atmosphericstability conditions, it is not until the plume has propagated more than100 m that its half-width is of order 20 m in size.

The problem of detecting a gas leak by the spatial profile of themeasured methane concentration thus involves:

1) Being insensitive to large-scale structure, which may be attributedto the background variations.

2) Detecting local enhancements in the methane concentration above thebackground consisting of peaks with half-widths in the approximate rangeof 1 m to 20m.

3) Rejecting noise in the measurement due to instrumental imperfections.

The basic idea of this exemplary approach is to convolve the inputconcentration as a function of distance f(x) with a collection ofGaussian kernelsg(x,w)=exp(−x ²/2w)/√{square root over (2πw)}  (1)for a variety of scales specified by the parameter w (here w hasdimensions of length squared). If we define L (x,w) to be theconvolution of f(x) and g(x,w), the normalized second derivative−w(∂²L/∂²) is sensitive to structures in f of spatial extentproportional to √{square root over (w)}. For example, if f(x) is aGaussian peak of half-width σ, i.e., f(x)=exp(−x²/2σ²)/(σ√{square rootover (2π)}), we find that

$\begin{matrix}{{- {w\left( \frac{\partial^{2}L}{\partial x^{2}} \right)}} = {{\frac{w}{\sqrt{2\pi}}\left\lbrack \frac{w + \sigma^{2} - x^{2}}{\left( {w + \sigma^{2}} \right)^{5/2}} \right\rbrack}{\exp\left\lbrack {- \frac{x^{2}}{2\left( {w + \sigma^{2}} \right)}} \right\rbrack}}} & (2)\end{matrix}$which has a maximum at x=0 and w=2σ². The value of the maximum is about0.385 times the amplitude of the original peak in f. Away from the peak,this falls smoothly to zero.

The basis of the algorithm is to calculate the surface −w(∂²L/∂x²) andto examine the result for local maxima in both x and w. For each maximum({circumflex over (x)}, ŵ) the position x₀ and half-width w₀ of thecorresponding peak are reported as x₀={circumflex over (x)} andw₀=√{square root over (ŵ/2)}, and the peak amplitude is scaled from thevalue of the surface at the maximum. Only a range of w is considered,corresponding to a range of peak half-widths of typically 1 m to 20 mthat correspond to plume dimensions seen in leak detection.

Several mathematical properties allow for the more convenientcalculation of the above space-scale surface. Since the Gaussian kernelssatisfy ∂g/∂w=½∂²g/∂x², it is possible to compute the surface as theconvolution of −2w(∂g/∂w) and the input function f(x). A finite numberof values of w are used in practice, spaced geometrically, namely wε{w₁,w₂, . . . , w_(n)} where w_(i)=λ^((i-1))w₁ for some λ>1. The partialderivative of g with respect to w can also be approximated by a finitedifference, and the convolutions computed as discrete summations.

It is possible to organize the computation of the space-scale surface ina pipelined manner, so that a stream of samples of f(x) is used asinput. The convolutions can be evaluated lazily so that at any stage,only enough samples of the surface are produced as are needed todetermine whether a point on the surface is a local maximum. Once thatdetermination has taken place, samples which are no longer needed arediscarded, so that the entire calculation can take place in near realtime in a limited amount of memory.

Having obtained the locations, amplitudes and widths of candidate peaks,an additional filtering step can be applied which selects amplitudesabove a certain threshold (or within a certain range). As described ingreater detail in section D below, the remaining peaks can be displayedas leak indications, using icons whose sizes indicate the amplitude ofthe peak, and whose positions on a map indicate where along the path thepeak was located.

B) Tracers

We have found that source identification can be performed using isotopicratio measurements. For example, methane isotopic ratios (δD of CH₄ (‰)relative to Vienna standard mean ocean water (VSMOW), and δ¹³C of CH₄(‰) relative to Vienna Pee Dee Belemnite (VPDB)) fall in characteristicranges depending on the source of the methane. For near-surfacemicrobial gas (e.g., marsh gas, landfill gas), these ranges are about−350<δD<−260 and −63<δ¹³C<−40. For sub-surface microbial gas (e.g., deepsea sediments and drift gas), these ranges are about −250<δD<−170 and−90<δ¹³C<−60. For thermogenic gas (e.g., natural gas and coalbed gas),these ranges are about −250<δD<−100 and −57<δ¹³C<−28. Since these rangesbasically do not overlap, isotope ratio measurements can be used toprovide source identification for methane. It is expected that isotoperatio source identification is applicable in general for leakmeasurements of any gas.

Thus, a method according to this aspect of the invention starts withprimary gas concentration measurements from a moving platform asdescribed above (horizontal spatial scale analysis can be performed oromitted from the above-described methods). One or more secondary isotoperatio measurements from the moving platform are also performed from themoving platform. The secondary isotope ratio measurements are used toprovide source identification, while the primary gas concentrationmeasurements are used to determine presence/absence of a gas leak. Theseresults are provided to an end user.

Optionally, chemical tracer analysis can be performed in addition to theisotope ratio analysis for source identification. For example, naturalgas tagged with mercaptans can use the mercaptans as a chemical tracerto distinguish vs. other sources of natural gas, in combination with theisotopic ratio source identification.

Significant attributes of the tracer measurements (i.e., chemical and/orisotope ratio) are as follows:

1) The tracer should be present in the emitted gas in a known, constantratio to the primary constituent for all likely leaks within a targetmeasurement area. This ratio represents a ‘signature’ of the targetsource.

2) The ratio signature of the target emission source should be differentand distinguishable from other common sources of the primary constituentthat can be found in the target measurement area.

3) The ratio signature of the target emission source should also bedifferent from the ratio signature for the ambient background.

For example, for the case of methane as the primary concentrationmeasurement, other common sources of methane in an urban environment aresewer systems, landfills, petrochemical processing facilities, or otherindustrial activity. An example of a useful tracer for methane is thestable isotope ratio of carbon (¹³C/¹²C) in the methane sample. Naturalgas is an example of a petrogenic source of methane, which has adifferent stable isotope ratio than the biogenic gas emitted by from thesewer system, storm drains, or landfills, for example.

Other candidate tracer species include but are not limited to thehydrogen stable isotope ratio ²H/¹H, hydrogen sulfide or other odorantsin the natural gas; or ethane, propane, or other hydrocarbons.

Optionally, multiple tracers give additional dimensionality, allowingfor even more effective methods of distinguishing target sources fromother sources of the primary constituent.

Any approach for performing the isotope ratio analysis can be employed.One preferred approach is to perform source identification according tothe y-intercept of a linear fit of isotope ratio vs. inverseconcentration (known as a Keeling plot). FIGS. 3a-b show examples ofsuch plots. The example of FIG. 3a shows a typical signal for a leakdetection (y intercept differs from background level). The example ofFIG. 3b shows a typical background signal (y intercept same asbackground level).

The basic principles of a Keeling plot analysis are as follows. For asingle tracer, this ratio will vary from the background value in ambientair to a value that approaches, but does not reach, the ratio found inthe pure emission, due to the fact that the observed ratio is due to amixture of background gas and emissions. An analysis called a Keelingplot (developed by Charles Keeling for the analysis of carbon 13 presentin atmospheric carbon dioxide) can be used to clearly identifybackground and source, by plotting the tracer ratio as a function of theinverse of the observed primary concentration. The intercept of thisgraph is the tracer ratio of the emission source. If this value can bedistinguished from other possible sources, then an unambiguous sourcedetermination can be made. In this instance, ‘distinguished from’ meansthat the intercept determined from the plot does not differ from theexpected source signature in a statistically significant manner. Formultiple tracers, the Keeling method can be extended to multiple tracerratios.

The Keeling methods are best applied when the tracer measurements can bemade in real time. For flask type measurements where the number of datapoints are more limited, the Keeling method can still be applied forsource determination, as long as care as taken to collect flask samplesboth at or near the peak, and on the baseline nearby, where theconcentration levels have returned to ambient but not so far away thatother sources of the primary concentration or the tracer are influencingthe results.

Alternatively, an approach by J. B. Miller and P. P. Tans (Tellus, 55B,207-214, hereby incorporated by reference in its entirety) may beemployed, in which the tracer concentration is plotted as a function ofthe primary concentration and a linear regression is performed on thesedata. The slope of the line of best fit is used to estimate the tracerratio of the gas leak. An advantage of this method is that theuncertainty in the tracer concentration often does not vary with theprimary concentration, allowing the use of a simpler unweighted linearregression algorithm.

In some cases, a gas handling system can be employed in connection withthe secondary isotope ratio measurements. For example, a gas handlingsystem can be used to acquire one or more samples and to provide theacquired samples to an off-line isotope ratio measuring instrument. Hereoff-line indicates that the isotope ratio measurements are typicallysignificantly slower than the primary gas concentration measurements asdescribed above. Thus, the isotope ratio measurement are off-line withrespect to the time scale of the primary concentration measurements.However, the isotope ratio measurement instrument is preferably disposedon the moving platform. Acquired isotope ratio samples can be analyzedon-board. FIG. 4 shows an example. Here a gas handling system 406 storesa sample acquired at inlet 402 in chamber 404, and is capable ofproviding the contents of chamber 404 to instrument 104 (here instrument104 is an isotope ratio instrument).

Any approach for performing secondary isotope ratio measurements can beemployed. If chemical tracers are measured as well, any approach forsuch tracer measurements can be employed. Preferably, optical absorptionspectroscopy as described above is employed. Stationary measurements canbe used in addition to the primary gas concentration measurements, asdescribed above.

Secondary isotope ratio measurements can be combined with multi-pointmeasurements and analysis as described in section C below. The resultingautomatic transverse spatial scale analysis can provide a distanceestimate to a leak source.

C) Multi-Point Measurements for Distance Estimation

We have found that multi-point measurements can be useful for providingan estimate of distance to the leak source. Here, a multi-pointmeasurement is any measurement from two or more points on the movingplatform that are transversely separated from each other. FIGS. 5a-cshow example of transverse separation. Let z be the direction ofplatform travel, y be the vertical direction and x be perpendicular to yand z. FIG. 5a shows transversely separated measurement points 502 and504 where the separation is entirely in the x direction. FIG. 5b showstransversely separated measurement points 502 and 504 where theseparation is partly in the x direction and partly in the z direction.Points 502 and 504 on FIG. 5b are transversely separated because thereis a non-zero separation in the x direction. FIG. 5c shows transverselyseparated measurement points 502 and 504 where the separation isentirely in the y direction. Here the measurement points are disposed ona mast 506.

A method according to this aspect of the invention starts with primarygas concentration measurements from a moving platform as described above(horizontal spatial scale analysis can be performed or omitted from theabove-described methods). The multi-point measurements are used toprovide a distance estimate. More specifically, a distance between aplatform measurement position and a leak source location is estimated,where the platform measurement position is the platform position at thetime the relevant measurements were performed. Automatic spatial scaleanalysis of the multi-point measurements is used to provide thisdistance estimate. Results, including the distance estimate, can beprovided to an end user in various ways. Preferably, the measurementpoints are separated from each other vertically (e.g., as in the exampleof FIG. 5c ). The spatial scale analysis can include providing a gasleak plume model and inverting this model to determine a source distancefrom a measured concentration gradient. Note that this measuredconcentration gradient can be determined from multi-point measurementsas considered herein. Real-time atmospheric data can be included in thegas plume model.

Various gas handling approaches can be employed in connection withmulti-point measurements. The underlying requirement is to obtainsimultaneous or nearly simultaneous (i.e., preferably within about 5seconds, more preferably within about 1 second) measurements. FIGS. 6a-cshow examples of various two-point measurements. All of these approachescan be extended to measurements at any number of transversely separatedpoints. The example of FIG. 6a shows two instruments 104 a and 104 bhaving separated inlets 502 and 504. This can clearly providesimultaneous measurements, but has the disadvantage of increasing costby duplicating the measurement instrument. The example of FIG. 6b showsa single instrument 104 connected to inlets 502 and 504 via a switch602. If the switch and instrument are sufficiently fast, this approachcan provide nearly simultaneous measurements at the inlets. The exampleof FIG. 6c shows a gas handling system 604 having separated inlets 502and 504 that is capable of providing simultaneously or nearlysimultaneously acquired samples to a single instrument in sequence. Forexample, samples acquired at inlets 502 and 504 can be stored inchambers 606 and 608 respectively, and provided to instrument 104 insequence. The time difference between analysis of chamber 606 andchamber 608 is not important.

Preferably, primary gas concentration measurements are performed withoptical absorption spectroscopy as described above. Stationary gasconcentration measurements as described above can also be employed.

C2) Multi-Point Example

We consider the following example of using multiple verticalmeasurements of a plume to quantify the distance from the measurement tothe upwind source location. One well-validated physical model for aplume, developed by Gifford in 1959, is to model the plume as a Gaussiandistribution in the spatial dimensions transverse to the wind direction,or (for a ground level source)

$\begin{matrix}{{C\left( {x,y,z} \right)} = {\frac{Q}{\pi\; V\;\sigma_{y}\sigma_{z}}{\exp\left( {{{y^{2}/2}\sigma_{y}^{2}} - {{z^{2}/2}\sigma_{z}^{2}}} \right)}}} & (3)\end{matrix}$

As expected, the dimensions of the Gaussian distribution horizontallyand vertically (i.e., σ_(y) and σ_(z) respectively) increase withincreasing distance, and the amount they increase can be estimated frommeasurements of wind speed, solar irradiation, ground albedo, humidity,and terrain and obstacles, all of which influence the turbulent mixingof the atmosphere. However, if one is willing to tolerate somewhat moreuncertainty in the distance estimation, the turbulent mixing of theatmosphere can be estimated simply from the wind speed, the time of day,and the degree of cloudiness, all of which are parameters that areavailable either on the platform or from public weather databases inreal time. Using these available data, estimates of the Gaussian widthparameters can be estimated (e.g., by using the Pasquill-Gifford-Turnerturbulence typing scheme, or modified versions of this scheme). Forexample, one possible functional form for σ_(y) and σ_(z) isσ=ax/√{square root over (1+bx)}, where a and b are fitting parametersand x is distance along the plume axis. Separate fits can be performedfor the y and z directions, or the same fit can be used for bothdirections.

The multi-point vertical measurement can be used to estimate thevertical Guassian width. The horizontal width can also be estimated fromhorizontal spatial scale analysis, but the vertical analysis has theadvantage that the vertical extent of the plume is not as stronglydistorted by the motion of the platforms and other nearby platforms asis the horizontal dimension, where the plume can be carried alonghorizontally by the motion of a platform.

Given the estimate of the vertical Gaussian width, available look uptables can be used to determine the distance from the source given thewidth and the available information about the turbulent mixing of theatmosphere. Even without any atmospheric measurements whatsoever, acrude distance estimate can be determined (such as on-road, near-road,or far), which would provide valuable additional information to aninspector searching for the source of the emissions.

Other forms of multi-point analysis can be performed, as an alternativeto using a plume model. For example, for on road sources, a measurementpoint at or near the road surface (e.g., within 25 cm) which is close tothe emission source will see a dramatically different concentration thana measurement point on a 2-3 meter mast. In this situation, the Gaussianplume model breaks down, and a threshold analysis such as (delta peakheight)/(average peak height)>t (where the threshold t is the order of0.5) can unambiguously identify such sources as local (e.g., on-road).

D) User Interface

FIG. 7 shows an exemplary user interface relating to embodiments of theinvention. Here a map display 302 has leak figures (e.g., 704, 708, 710)superposed on it. The leak figures include pointers (e.g., 706) thatshow the location of detected leaks (i.e., platform positions at whichthe corresponding leak concentrations were measured). The figure sizecan be scaled according to quantities such as the peak amplitude (i.e.,the amount by which the peak concentration exceeds the localbackground), the peak concentration, or the measured spatial width ofthe peak. Numerical parameters (such as the amplitude, concentration,width, or a severity rank of the leak within some defined region, etc.)can be displayed inside the leak figures. If isotope ratio measurementsare performed, they can also be shown on the display (e.g., 712). Theleak figures can have any shape. The pointers on the leak figures canhave any shape.

The invention claimed is:
 1. A method of gas leak detection andlocalization, the method comprising: performing one or more primary gasconcentration measurements from a moving platform that proceeds along atleast one platform track disposed in proximity to one or more potentialgas leak locations; wherein the primary gas concentration measurementsare performed at two or more measurement points on the moving platformas it proceeds along the at least one platform track, wherein themeasurement points are transversely separated from each other withrespect to the platform track; performing an automatic spatial scaleanalysis of the primary gas concentration measurements; automaticallydetermining whether or not a gas leak is present at the potential gasleak locations based on the primary gas concentration measurements andthe automatic spatial scale analysis; wherein the automatic spatialscale analysis includes providing an estimated distance between aplatform measurement position and a leak source using the primary gasconcentration measurements, based on spatial analysis of results fromthe measurement points when the platform is at the platform measurementposition; and providing a leak indication at the potential gas leaklocations to an end user and providing the estimated distance to the enduser; wherein the primary gas concentration measurements aremeasurements of local gas concentration at the measurement points. 2.The method of claim 1, wherein the measurement points are verticallyseparated from each other.
 3. The method of claim 1, wherein the spatialscale analysis comprises providing a gas leak plume model, and invertingthe gas leak plume model to determine a source distance from a measuredconcentration gradient.
 4. The method of claim 3, further comprisingincluding real time measured atmospheric data into the gas plume model.5. The method of claim 1, wherein the spatial scale analysis comprisesproviding a predetermined threshold and reporting a leak as local if adifference in measured concentration at the measurement points exceedsthe predetermined threshold.
 6. The method of claim 1, whereinsimultaneous primary gas concentration measurements are provided by twoor more instruments having separated inlets.
 7. The method of claim 1,wherein simultaneous primary gas concentration measurements are providedby a gas handling system having two or more separated inlets and capableof providing two or more simultaneously acquired samples to a singleinstrument in sequence.
 8. The method of claim 1, wherein simultaneousprimary gas concentration measurements are provided by switching asingle instrument between two or more separated inlets.
 9. The method ofclaim 1, wherein the primary gas concentration measurements are opticalabsorption measurements made in a resonant optical cavity disposed in aninstrument in the moving platform.
 10. The method of claim 1, furthercomprising performing one or more stationary gas concentrationmeasurements when the platform is stationary.
 11. The method of claim 1,wherein the leak indication is a binary yes/no indication of whether ornot a leak is present.
 12. The method of claim 1, further comprisingproviding a display showing a map with leak figures superposed on themap to show detected leaks, wherein the leak figures include pointersshowing the locations of detected leaks, and wherein sizes of the leakfigures are scaled with a difference between the measured concentrationand a local background level.
 13. The method of claim 1, furthercomprising providing a display showing a map with leak figuressuperposed on the map to show detected leaks, wherein the leak figuresinclude pointers showing the locations of detected leaks, and whereinsizes of the leak figures are scaled with the measured concentration.